Can RTX 4090 run Phi-4 Mini 3.8B?

Yes — runs locally

~144 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

3.8B

Best quant

Q8_0

VRAM needed

4.3 GB

The verdict

The RTX 4090 (24 GB VRAM) handles Phi-4 Mini 3.8B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 144 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Latest Phi mini with strong reasoning. Drop-in upgrade from Phi-3.5 Mini.

Setup tutorial: Phi-4 Mini 3.8B on RTX 4090

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Phi-4 Mini 3.8B runs at Grade S on an NVIDIA GeForce RTX 4090 with Q8_0 quantization, achieving ~265 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 installed.

Expected performance

With the Q8_0 quantization, you can expect the model to run at approximately 265 tokens per second, using 4.3GB of VRAM. Given the remaining 19.7GB of VRAM, you can achieve a practical context window of up to 131072 tokens, which is the maximum supported by the model.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q8_0 quantized model (3.8GB file) from Hugging Face.

ollama pull bartowski/microsoft_Phi-4-mini-instruct-GGUF:microsoft_Phi-4-mini-instruct-Q8_0.gguf

3. Run it

ollama run --model=microsoft_Phi-4-mini-instruct-Q8_0 --interactive
ollama chat --model=microsoft_Phi-4-mini-instruct-Q8_0

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 38 (the total number of layers in the model). Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 4.3GB VRAM used by the model, you have 19.7GB of VRAM left for context, allowing for a practical context window of up to 131072 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of layers loaded on the GPU by setting --n-gpu-layers to a lower value, such as 30.

Slow token generation

Ensure that flash attention is enabled with --flash-attn. If still slow, try reducing the batch size or increasing the number of threads.

Model not loading

Check that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is a good choice for a more user-friendly interface, while llama.cpp offers more control over optimizations and is suitable for advanced users. Jan is another lightweight option that can be used for quick testing and prototyping. For the NVIDIA GeForce RTX 4090, Ollama is recommended due to its ease of use and robust performance.

Full Phi-4 Mini 3.8B details →

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FAQ (20)

What GPU do I need to run Phi-4 Mini 3.8B?

To run Phi-4 Mini 3.8B, you need a GPU with at least 2.8 GB of VRAM, but 4.3 GB is recommended for optimal performance, especially with higher quantization levels.

Is Phi-4 Mini 3.8B good for coding?

Yes, Phi-4 Mini 3.8B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 131,072 tokens, which allows it to handle complex code snippets and documentation.

Phi-4 Mini 3.8B vs Llama 3.1 8B?

Phi-4 Mini 3.8B has fewer parameters (3.8B vs 8B) but is more efficient in terms of VRAM usage and performance, making it a better choice for systems with limited resources. It also offers a larger context length of 131,072 tokens compared to Llama 3.1 8B.

Can I run Phi-4 Mini 3.8B on a Mac?

Yes, you can run Phi-4 Mini 3.8B on a Mac, provided your Mac has a compatible GPU with at least 2.8 GB of VRAM. Ensure you have the necessary drivers and software installed for optimal performance.

How much VRAM does Phi-4 Mini 3.8B need?

Phi-4 Mini 3.8B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM but offer better performance.

Is Phi-4 Mini 3.8B censored?

Phi-4 Mini 3.8B is not inherently censored, but it may include content filters or safeguards to prevent the generation of harmful or inappropriate content, as is common in many AI models.

Is Phi-4 Mini 3.8B commercial-use allowed?

Yes, Phi-4 Mini 3.8B is licensed under the MIT License, which allows for both personal and commercial use without additional restrictions.

Phi-4 Mini 3.8B context length?

Phi-4 Mini 3.8B has a context length of 131,072 tokens, which is significantly larger than many other models, allowing it to process and generate longer sequences of text.

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